Epidemic outbreaks in complex heterogeneous networks

By Yamir Moreno, Romualdo Pastor-Satorras, Alessandro Vespignani. 2001

1,109 citations recorded by the source corpus.

Abstract:We present a detailed analytical and numerical study for the spreading of infections with acquired immunity in complex population networks. We show that the large connectivity fluctuations usually found in these networks strengthen considerably the incidence of epidemic outbreaks. Scale-free networks, which are characterized by diverging connectivity fluctuations in the limit of a very large number of nodes, exhibit the lack of an epidemic threshold and always show a finite fraction of infected individuals. This particular weakness, observed also in models without immunity, defines a new epidemiological framework characterized by a highly heterogeneous response of the system to the introduction of infected individuals with different connectivity. The understanding of epidemics in complex networks might deliver new insights in the spread of information and diseases in biological and technological networks that often appear to be characterized by complex heterogeneous architectures.

Subfields: Biomedical Engineering, Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/100b4cda5d02483353a218f4c3d5c9c26e1406ab

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Citations in the corpus

The Structure and Function of Complex Networks (2003, methodology): “The mean-field method can also be applied to the SIR model [24, 299].”
Theory of Rumour Spreading in Complex Social Networks (2007, methodology): “This can be done for example, as for the SIR model [28].”
Evolution of networks (2001, result): “ Nevertheless, the main parameter of interest describing the spread of the disease is the same, that is, λ, and main results for the SIR model on networks [208] are similar to those for the SIS model [206,207,209], so here we discuss the simpler case. Thus, we speak about the SIS model on equilibriu”
Epidemic spreading in real networks: an eigenvalue viewpoint (2003, result): “We show that our model conforms better to simulation results than previous models over real networks.”
The Anatomy of a Scientific Rumor (2013, background): “[27] Moreno, Y.”
Statistical physics of vaccination (2016, positive): “Based on the fact that in heterogenous networks βIc = 〈k〉/〈k(2)〉 for the SIS model and βIc = 〈k〉/(〈k(2)〉− 〈k〉) for the SIR model [145, 172, 291], and by using Eq.”
Critical phenomena in complex networks (2007, background): “Figure 18 shows how the average fraction of infected vertices evolves in the SIR model placed on the Barabási-Albert network if the first infected individual has exactly q neighbors (Moreno et al., 2002).”
Identifying influential spreaders in complex networks based on gravity formula (2015, methodology): “We employ the standard susceptible-infected-removed (SIR) model [21] to estimate the spreading influence of the nodes (labeled by R).”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, positive): “This result w as modified in subsequent papers [480, 481], where the authors used analytical arguments to s how that in absence of higher order correlations, the epidemic threshold is null for scal e-free networks with2 < γ ≤ 3.”
Synchronization in complex networks (2008, background): “Notably, the latter equation is exactly the same found for the absence of critical behavior in the region 2 γ 3 for a model of epidemic spreading (Moreno et al., 2002).”